Triple
T1607214
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marla Maples |
E34532
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
|
E181993
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Marla | Statement: [Marla Maples, givenName, Marla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marla Context triple: [Marla Maples, givenName, Marla]
-
A.
Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
-
B.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
-
C.
Wilella
Wilella is the full given name of American novelist Willa Cather, renowned for her works depicting frontier life on the Great Plains.
-
D.
Brielle
Brielle is a historic fortified town in the Dutch province of South Holland, known for its well-preserved medieval center and role in the Eighty Years' War.
-
E.
Korina
Korina is the surname of Irina Korina, a contemporary Russian artist known for her installations and sculptural works.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Marla Triple: [Marla Maples, givenName, Marla]
Generated description
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Marla Target entity description: Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
A.
Verna
Verna is a feminine given name that gained particular recognition through film editor Verna Fields, known for her work on movies like "Jaws."
-
B.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
-
C.
Wilella
Wilella is the full given name of American novelist Willa Cather, renowned for her works depicting frontier life on the Great Plains.
-
D.
Brielle
Brielle is a historic fortified town in the Dutch province of South Holland, known for its well-preserved medieval center and role in the Eighty Years' War.
-
E.
Korina
Korina is the surname of Irina Korina, a contemporary Russian artist known for her installations and sculptural works.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a885fea6a481909fe83ba6441f1774 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9096cd0b88190bac21b46c3ed453f |
completed | March 5, 2026, 4:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad51bff1cc819082208a8a77fae631 |
completed | March 8, 2026, 10:38 a.m. |
| NEDg | Description generation | batch_69ad522beb488190b5157db37eb0da8e |
completed | March 8, 2026, 10:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad529de3dc819081c8ad3d7aa8bef8 |
completed | March 8, 2026, 10:42 a.m. |
Created at: March 4, 2026, 7:28 p.m.